Overview

Dataset statistics

Number of variables39
Number of observations9879
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 MiB
Average record size in memory312.0 B

Variable types

Categorical11
Numeric28

Alerts

blueKills is highly overall correlated with blueAssists and 7 other fieldsHigh correlation
blueDeaths is highly overall correlated with blueGoldDiff and 7 other fieldsHigh correlation
blueAssists is highly overall correlated with blueKills and 5 other fieldsHigh correlation
blueTotalGold is highly overall correlated with blueKills and 9 other fieldsHigh correlation
blueAvgLevel is highly overall correlated with blueTotalGold and 6 other fieldsHigh correlation
blueTotalExperience is highly overall correlated with blueTotalGold and 8 other fieldsHigh correlation
blueTotalMinionsKilled is highly overall correlated with blueTotalExperience and 1 other fieldsHigh correlation
blueGoldDiff is highly overall correlated with blueKills and 18 other fieldsHigh correlation
blueExperienceDiff is highly overall correlated with blueKills and 14 other fieldsHigh correlation
blueCSPerMin is highly overall correlated with blueTotalExperience and 1 other fieldsHigh correlation
blueGoldPerMin is highly overall correlated with blueKills and 9 other fieldsHigh correlation
redKills is highly overall correlated with blueDeaths and 7 other fieldsHigh correlation
redDeaths is highly overall correlated with blueKills and 7 other fieldsHigh correlation
redAssists is highly overall correlated with blueDeaths and 5 other fieldsHigh correlation
redTotalGold is highly overall correlated with blueDeaths and 9 other fieldsHigh correlation
redAvgLevel is highly overall correlated with blueGoldDiff and 6 other fieldsHigh correlation
redTotalExperience is highly overall correlated with blueGoldDiff and 8 other fieldsHigh correlation
redTotalMinionsKilled is highly overall correlated with redTotalExperience and 1 other fieldsHigh correlation
redGoldDiff is highly overall correlated with blueKills and 18 other fieldsHigh correlation
redExperienceDiff is highly overall correlated with blueKills and 14 other fieldsHigh correlation
redCSPerMin is highly overall correlated with redTotalExperience and 1 other fieldsHigh correlation
redGoldPerMin is highly overall correlated with blueDeaths and 9 other fieldsHigh correlation
blueWins is highly overall correlated with blueGoldDiff and 1 other fieldsHigh correlation
blueFirstBlood is highly overall correlated with redFirstBloodHigh correlation
blueEliteMonsters is highly overall correlated with blueDragons and 1 other fieldsHigh correlation
blueDragons is highly overall correlated with blueEliteMonsters and 2 other fieldsHigh correlation
blueHeralds is highly overall correlated with blueEliteMonstersHigh correlation
blueTowersDestroyed is highly overall correlated with blueGoldDiff and 1 other fieldsHigh correlation
redFirstBlood is highly overall correlated with blueFirstBloodHigh correlation
redEliteMonsters is highly overall correlated with blueDragons and 2 other fieldsHigh correlation
redDragons is highly overall correlated with blueDragons and 1 other fieldsHigh correlation
redHeralds is highly overall correlated with redEliteMonstersHigh correlation
blueTowersDestroyed is highly imbalanced (87.3%)Imbalance
redTowersDestroyed is highly imbalanced (83.7%)Imbalance
blueWardsDestroyed has 745 (7.5%) zerosZeros
blueAssists has 217 (2.2%) zerosZeros
redWardsDestroyed has 785 (7.9%) zerosZeros
redAssists has 235 (2.4%) zerosZeros

Reproduction

Analysis started2023-05-10 19:19:11.543945
Analysis finished2023-05-10 19:21:03.616361
Duration1 minute and 52.07 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

blueWins
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
0
4949 
1
4930 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4949
50.1%
1 4930
49.9%

Length

2023-05-10T16:21:03.929383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T16:21:04.083175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4949
50.1%
1 4930
49.9%

Most occurring characters

ValueCountFrequency (%)
0 4949
50.1%
1 4930
49.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4949
50.1%
1 4930
49.9%

Most occurring scripts

ValueCountFrequency (%)
Common 9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4949
50.1%
1 4930
49.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4949
50.1%
1 4930
49.9%

blueWardsPlaced
Real number (ℝ)

Distinct147
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.288288
Minimum5
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:04.215113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile12
Q114
median16
Q320
95-th percentile53
Maximum250
Range245
Interquartile range (IQR)6

Descriptive statistics

Standard deviation18.019177
Coefficient of variation (CV)0.80845942
Kurtosis23.439452
Mean22.288288
Median Absolute Deviation (MAD)2
Skewness4.1363526
Sum220186
Variance324.69072
MonotonicityNot monotonic
2023-05-10T16:21:04.396581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 1255
12.7%
15 1217
12.3%
17 988
10.0%
14 974
 
9.9%
18 831
 
8.4%
13 694
 
7.0%
19 483
 
4.9%
12 447
 
4.5%
20 288
 
2.9%
11 239
 
2.4%
Other values (137) 2463
24.9%
ValueCountFrequency (%)
5 2
 
< 0.1%
7 1
 
< 0.1%
8 16
 
0.2%
9 39
 
0.4%
10 96
 
1.0%
11 239
 
2.4%
12 447
 
4.5%
13 694
7.0%
14 974
9.9%
15 1217
12.3%
ValueCountFrequency (%)
250 1
< 0.1%
221 1
< 0.1%
209 1
< 0.1%
203 1
< 0.1%
198 1
< 0.1%
185 1
< 0.1%
183 1
< 0.1%
176 1
< 0.1%
167 1
< 0.1%
165 1
< 0.1%

blueWardsDestroyed
Real number (ℝ)

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8248811
Minimum0
Maximum27
Zeros745
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:04.574231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum27
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1749984
Coefficient of variation (CV)0.76994335
Kurtosis17.196758
Mean2.8248811
Median Absolute Deviation (MAD)1
Skewness2.8459816
Sum27907
Variance4.730618
MonotonicityNot monotonic
2023-05-10T16:21:04.745031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
2 2357
23.9%
3 2116
21.4%
1 1790
18.1%
4 1413
14.3%
5 746
 
7.6%
0 745
 
7.5%
6 345
 
3.5%
7 163
 
1.6%
8 68
 
0.7%
9 22
 
0.2%
Other values (17) 114
 
1.2%
ValueCountFrequency (%)
0 745
 
7.5%
1 1790
18.1%
2 2357
23.9%
3 2116
21.4%
4 1413
14.3%
5 746
 
7.6%
6 345
 
3.5%
7 163
 
1.6%
8 68
 
0.7%
9 22
 
0.2%
ValueCountFrequency (%)
27 1
 
< 0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 2
 
< 0.1%
21 2
 
< 0.1%
20 3
 
< 0.1%
19 9
0.1%
18 11
0.1%
17 10
0.1%

blueFirstBlood
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
1
4987 
0
4892 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 4987
50.5%
0 4892
49.5%

Length

2023-05-10T16:21:04.947245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T16:21:05.076336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1 4987
50.5%
0 4892
49.5%

Most occurring characters

ValueCountFrequency (%)
1 4987
50.5%
0 4892
49.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4987
50.5%
0 4892
49.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4987
50.5%
0 4892
49.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4987
50.5%
0 4892
49.5%

blueKills
Real number (ℝ)

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1839255
Minimum0
Maximum22
Zeros63
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:05.169895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median6
Q38
95-th percentile12
Maximum22
Range22
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.011028
Coefficient of variation (CV)0.48691207
Kurtosis0.2637882
Mean6.1839255
Median Absolute Deviation (MAD)2
Skewness0.53851754
Sum61091
Variance9.0662895
MonotonicityNot monotonic
2023-05-10T16:21:05.304164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
6 1322
13.4%
5 1302
13.2%
4 1186
12.0%
7 1138
11.5%
8 942
9.5%
3 917
9.3%
9 717
7.3%
2 609
6.2%
10 527
 
5.3%
11 340
 
3.4%
Other values (11) 879
8.9%
ValueCountFrequency (%)
0 63
 
0.6%
1 313
 
3.2%
2 609
6.2%
3 917
9.3%
4 1186
12.0%
5 1302
13.2%
6 1322
13.4%
7 1138
11.5%
8 942
9.5%
9 717
7.3%
ValueCountFrequency (%)
22 1
 
< 0.1%
19 2
 
< 0.1%
18 4
 
< 0.1%
17 13
 
0.1%
16 30
 
0.3%
15 38
 
0.4%
14 64
 
0.6%
13 147
1.5%
12 204
2.1%
11 340
3.4%

blueDeaths
Real number (ℝ)

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1376658
Minimum0
Maximum22
Zeros72
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:05.431524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median6
Q38
95-th percentile11
Maximum22
Range22
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9338177
Coefficient of variation (CV)0.4780022
Kurtosis0.21409761
Mean6.1376658
Median Absolute Deviation (MAD)2
Skewness0.50749282
Sum60634
Variance8.6072864
MonotonicityNot monotonic
2023-05-10T16:21:05.543576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
5 1341
13.6%
6 1293
13.1%
4 1221
12.4%
7 1188
12.0%
8 942
9.5%
3 934
9.5%
9 734
7.4%
2 603
6.1%
10 494
 
5.0%
11 331
 
3.4%
Other values (11) 798
8.1%
ValueCountFrequency (%)
0 72
 
0.7%
1 270
 
2.7%
2 603
6.1%
3 934
9.5%
4 1221
12.4%
5 1341
13.6%
6 1293
13.1%
7 1188
12.0%
8 942
9.5%
9 734
7.4%
ValueCountFrequency (%)
22 1
 
< 0.1%
19 2
 
< 0.1%
18 2
 
< 0.1%
17 8
 
0.1%
16 20
 
0.2%
15 32
 
0.3%
14 66
 
0.7%
13 114
 
1.2%
12 211
2.1%
11 331
3.4%

blueAssists
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6451058
Minimum0
Maximum29
Zeros217
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:05.666310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median6
Q39
95-th percentile14
Maximum29
Range29
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.0645199
Coefficient of variation (CV)0.61165616
Kurtosis1.1591145
Mean6.6451058
Median Absolute Deviation (MAD)3
Skewness0.89026119
Sum65647
Variance16.520322
MonotonicityNot monotonic
2023-05-10T16:21:05.822364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
5 1068
10.8%
4 1010
10.2%
6 935
9.5%
3 926
9.4%
7 880
8.9%
8 843
8.5%
2 731
 
7.4%
9 648
 
6.6%
10 541
 
5.5%
1 468
 
4.7%
Other values (20) 1829
18.5%
ValueCountFrequency (%)
0 217
 
2.2%
1 468
4.7%
2 731
7.4%
3 926
9.4%
4 1010
10.2%
5 1068
10.8%
6 935
9.5%
7 880
8.9%
8 843
8.5%
9 648
6.6%
ValueCountFrequency (%)
29 2
 
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
26 3
 
< 0.1%
25 7
0.1%
24 6
 
0.1%
23 7
0.1%
22 12
0.1%
21 11
0.1%
20 15
0.2%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
0
5156 
1
4013 
2
710 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 5156
52.2%
1 4013
40.6%
2 710
 
7.2%

Length

2023-05-10T16:21:06.043875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T16:21:06.192598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5156
52.2%
1 4013
40.6%
2 710
 
7.2%

Most occurring characters

ValueCountFrequency (%)
0 5156
52.2%
1 4013
40.6%
2 710
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5156
52.2%
1 4013
40.6%
2 710
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5156
52.2%
1 4013
40.6%
2 710
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5156
52.2%
1 4013
40.6%
2 710
 
7.2%

blueDragons
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
0
6303 
1
3576 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6303
63.8%
1 3576
36.2%

Length

2023-05-10T16:21:06.289759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T16:21:06.407493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 6303
63.8%
1 3576
36.2%

Most occurring characters

ValueCountFrequency (%)
0 6303
63.8%
1 3576
36.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6303
63.8%
1 3576
36.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6303
63.8%
1 3576
36.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6303
63.8%
1 3576
36.2%

blueHeralds
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
0
8022 
1
1857 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 8022
81.2%
1 1857
 
18.8%

Length

2023-05-10T16:21:06.499107image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T16:21:06.613335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 8022
81.2%
1 1857
 
18.8%

Most occurring characters

ValueCountFrequency (%)
0 8022
81.2%
1 1857
 
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8022
81.2%
1 1857
 
18.8%

Most occurring scripts

ValueCountFrequency (%)
Common 9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8022
81.2%
1 1857
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8022
81.2%
1 1857
 
18.8%

blueTowersDestroyed
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
0
9415 
1
 
429
2
 
27
3
 
7
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9415
95.3%
1 429
 
4.3%
2 27
 
0.3%
3 7
 
0.1%
4 1
 
< 0.1%

Length

2023-05-10T16:21:06.707705image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T16:21:06.821544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 9415
95.3%
1 429
 
4.3%
2 27
 
0.3%
3 7
 
0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 9415
95.3%
1 429
 
4.3%
2 27
 
0.3%
3 7
 
0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9415
95.3%
1 429
 
4.3%
2 27
 
0.3%
3 7
 
0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9415
95.3%
1 429
 
4.3%
2 27
 
0.3%
3 7
 
0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9415
95.3%
1 429
 
4.3%
2 27
 
0.3%
3 7
 
0.1%
4 1
 
< 0.1%

blueTotalGold
Real number (ℝ)

Distinct4739
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16503.456
Minimum10730
Maximum23701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:06.927609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10730
5-th percentile14194
Q115415.5
median16398
Q317459
95-th percentile19190.5
Maximum23701
Range12971
Interquartile range (IQR)2043.5

Descriptive statistics

Standard deviation1535.4466
Coefficient of variation (CV)0.093037887
Kurtosis0.47931151
Mean16503.456
Median Absolute Deviation (MAD)1016
Skewness0.46824752
Sum1.6303764 × 108
Variance2357596.4
MonotonicityNot monotonic
2023-05-10T16:21:07.074967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15885 9
 
0.1%
16749 9
 
0.1%
16956 9
 
0.1%
15967 8
 
0.1%
17951 8
 
0.1%
16940 8
 
0.1%
15649 8
 
0.1%
16688 8
 
0.1%
16291 7
 
0.1%
16972 7
 
0.1%
Other values (4729) 9798
99.2%
ValueCountFrequency (%)
10730 1
< 0.1%
12002 1
< 0.1%
12178 1
< 0.1%
12292 1
< 0.1%
12300 1
< 0.1%
12403 1
< 0.1%
12519 1
< 0.1%
12598 1
< 0.1%
12622 1
< 0.1%
12682 1
< 0.1%
ValueCountFrequency (%)
23701 1
< 0.1%
23424 1
< 0.1%
23359 1
< 0.1%
23349 1
< 0.1%
23335 1
< 0.1%
23278 1
< 0.1%
23205 1
< 0.1%
22845 1
< 0.1%
22745 1
< 0.1%
22697 1
< 0.1%

blueAvgLevel
Real number (ℝ)

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9160036
Minimum4.6
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:07.188035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum4.6
5-th percentile6.4
Q16.8
median7
Q37.2
95-th percentile7.4
Maximum8
Range3.4
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.30514582
Coefficient of variation (CV)0.044121698
Kurtosis1.1161667
Mean6.9160036
Median Absolute Deviation (MAD)0.2
Skewness-0.33850158
Sum68323.2
Variance0.093113973
MonotonicityNot monotonic
2023-05-10T16:21:07.275316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7 2611
26.4%
6.8 2442
24.7%
7.2 1779
18.0%
6.6 1339
13.6%
7.4 684
 
6.9%
6.4 578
 
5.9%
6.2 175
 
1.8%
7.6 174
 
1.8%
6 43
 
0.4%
7.8 28
 
0.3%
Other values (7) 26
 
0.3%
ValueCountFrequency (%)
4.6 1
 
< 0.1%
4.8 1
 
< 0.1%
5.2 2
 
< 0.1%
5.4 3
 
< 0.1%
5.6 4
 
< 0.1%
5.8 13
 
0.1%
6 43
 
0.4%
6.2 175
 
1.8%
6.4 578
5.9%
6.6 1339
13.6%
ValueCountFrequency (%)
8 2
 
< 0.1%
7.8 28
 
0.3%
7.6 174
 
1.8%
7.4 684
 
6.9%
7.2 1779
18.0%
7 2611
26.4%
6.8 2442
24.7%
6.6 1339
13.6%
6.4 578
 
5.9%
6.2 175
 
1.8%

blueTotalExperience
Real number (ℝ)

Distinct4143
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17928.11
Minimum10098
Maximum22224
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:07.381673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10098
5-th percentile15934.8
Q117168
median17951
Q318724
95-th percentile19826.1
Maximum22224
Range12126
Interquartile range (IQR)1556

Descriptive statistics

Standard deviation1200.5238
Coefficient of variation (CV)0.066963208
Kurtosis0.68036336
Mean17928.11
Median Absolute Deviation (MAD)779
Skewness-0.24858763
Sum1.771118 × 108
Variance1441257.3
MonotonicityNot monotonic
2023-05-10T16:21:07.509032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18530 12
 
0.1%
18159 10
 
0.1%
17908 9
 
0.1%
17857 9
 
0.1%
17592 9
 
0.1%
18495 9
 
0.1%
18597 9
 
0.1%
19116 9
 
0.1%
18073 9
 
0.1%
18750 9
 
0.1%
Other values (4133) 9785
99.0%
ValueCountFrequency (%)
10098 1
< 0.1%
10826 1
< 0.1%
11286 1
< 0.1%
11921 1
< 0.1%
12111 1
< 0.1%
12212 1
< 0.1%
12556 1
< 0.1%
12798 1
< 0.1%
13119 1
< 0.1%
13166 1
< 0.1%
ValueCountFrequency (%)
22224 1
< 0.1%
22125 1
< 0.1%
21898 1
< 0.1%
21800 1
< 0.1%
21701 1
< 0.1%
21650 1
< 0.1%
21642 1
< 0.1%
21625 1
< 0.1%
21588 1
< 0.1%
21575 1
< 0.1%

blueTotalMinionsKilled
Real number (ℝ)

Distinct148
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216.69956
Minimum90
Maximum283
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:07.637677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile180
Q1202
median218
Q3232
95-th percentile251
Maximum283
Range193
Interquartile range (IQR)30

Descriptive statistics

Standard deviation21.858437
Coefficient of variation (CV)0.10086978
Kurtosis0.17262236
Mean216.69956
Median Absolute Deviation (MAD)15
Skewness-0.26777078
Sum2140775
Variance477.79128
MonotonicityNot monotonic
2023-05-10T16:21:07.761424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
218 193
 
2.0%
220 192
 
1.9%
222 190
 
1.9%
229 186
 
1.9%
221 185
 
1.9%
214 183
 
1.9%
226 183
 
1.9%
215 175
 
1.8%
225 175
 
1.8%
223 175
 
1.8%
Other values (138) 8042
81.4%
ValueCountFrequency (%)
90 1
 
< 0.1%
120 1
 
< 0.1%
123 1
 
< 0.1%
130 1
 
< 0.1%
131 1
 
< 0.1%
136 1
 
< 0.1%
137 1
 
< 0.1%
138 2
< 0.1%
140 1
 
< 0.1%
141 3
< 0.1%
ValueCountFrequency (%)
283 1
 
< 0.1%
281 1
 
< 0.1%
279 2
 
< 0.1%
276 6
0.1%
275 1
 
< 0.1%
274 1
 
< 0.1%
273 1
 
< 0.1%
272 6
0.1%
271 6
0.1%
270 9
0.1%
Distinct74
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.509667
Minimum0
Maximum92
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:07.887465image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q144
median50
Q356
95-th percentile68
Maximum92
Range92
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.8982822
Coefficient of variation (CV)0.19596807
Kurtosis0.38532781
Mean50.509667
Median Absolute Deviation (MAD)6
Skewness0.11697916
Sum498985
Variance97.97599
MonotonicityNot monotonic
2023-05-10T16:21:08.023645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 816
 
8.3%
52 772
 
7.8%
44 690
 
7.0%
56 618
 
6.3%
60 484
 
4.9%
40 456
 
4.6%
51 332
 
3.4%
47 323
 
3.3%
64 315
 
3.2%
55 300
 
3.0%
Other values (64) 4773
48.3%
ValueCountFrequency (%)
0 2
 
< 0.1%
4 3
< 0.1%
6 1
 
< 0.1%
16 2
 
< 0.1%
18 2
 
< 0.1%
19 3
< 0.1%
20 6
0.1%
21 2
 
< 0.1%
22 2
 
< 0.1%
23 6
0.1%
ValueCountFrequency (%)
92 1
 
< 0.1%
88 1
 
< 0.1%
85 2
 
< 0.1%
84 4
 
< 0.1%
83 6
 
0.1%
82 2
 
< 0.1%
81 5
 
0.1%
80 19
0.2%
79 6
 
0.1%
78 2
 
< 0.1%

blueGoldDiff
Real number (ℝ)

Distinct6047
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.414111
Minimum-10830
Maximum11467
Zeros2
Zeros (%)< 0.1%
Negative4917
Negative (%)49.8%
Memory size77.3 KiB
2023-05-10T16:21:08.152745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-10830
5-th percentile-4033.2
Q1-1585.5
median14
Q31596
95-th percentile4074
Maximum11467
Range22297
Interquartile range (IQR)3181.5

Descriptive statistics

Standard deviation2453.3492
Coefficient of variation (CV)170.20469
Kurtosis0.2994089
Mean14.414111
Median Absolute Deviation (MAD)1592
Skewness0.030037509
Sum142397
Variance6018922.2
MonotonicityNot monotonic
2023-05-10T16:21:08.279513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
428 8
 
0.1%
1167 7
 
0.1%
-1806 7
 
0.1%
-839 6
 
0.1%
-27 6
 
0.1%
1060 6
 
0.1%
-635 6
 
0.1%
611 6
 
0.1%
-152 6
 
0.1%
-1208 6
 
0.1%
Other values (6037) 9815
99.4%
ValueCountFrequency (%)
-10830 1
< 0.1%
-10329 1
< 0.1%
-9341 1
< 0.1%
-9152 1
< 0.1%
-8472 1
< 0.1%
-8461 1
< 0.1%
-7952 1
< 0.1%
-7911 1
< 0.1%
-7868 1
< 0.1%
-7866 1
< 0.1%
ValueCountFrequency (%)
11467 1
< 0.1%
8977 1
< 0.1%
8863 1
< 0.1%
8776 1
< 0.1%
8667 1
< 0.1%
8657 1
< 0.1%
8553 1
< 0.1%
8532 1
< 0.1%
8450 1
< 0.1%
8347 1
< 0.1%

blueExperienceDiff
Real number (ℝ)

Distinct5356
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-33.620306
Minimum-9333
Maximum8348
Zeros1
Zeros (%)< 0.1%
Negative5014
Negative (%)50.8%
Memory size77.3 KiB
2023-05-10T16:21:08.396003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-9333
5-th percentile-3206.1
Q1-1290.5
median-28
Q31212
95-th percentile3109.3
Maximum8348
Range17681
Interquartile range (IQR)2502.5

Descriptive statistics

Standard deviation1920.3704
Coefficient of variation (CV)-57.119363
Kurtosis0.36484788
Mean-33.620306
Median Absolute Deviation (MAD)1252
Skewness0.022876036
Sum-332135
Variance3687822.6
MonotonicityNot monotonic
2023-05-10T16:21:08.514195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 8
 
0.1%
-1025 7
 
0.1%
411 7
 
0.1%
-298 7
 
0.1%
-226 7
 
0.1%
-29 7
 
0.1%
-1476 7
 
0.1%
-953 6
 
0.1%
1187 6
 
0.1%
-213 6
 
0.1%
Other values (5346) 9811
99.3%
ValueCountFrequency (%)
-9333 1
< 0.1%
-8531 1
< 0.1%
-8290 1
< 0.1%
-8242 1
< 0.1%
-7340 1
< 0.1%
-6488 1
< 0.1%
-6414 1
< 0.1%
-6365 1
< 0.1%
-6317 1
< 0.1%
-6210 1
< 0.1%
ValueCountFrequency (%)
8348 1
< 0.1%
8265 1
< 0.1%
7645 1
< 0.1%
7621 1
< 0.1%
7609 1
< 0.1%
6703 1
< 0.1%
6558 1
< 0.1%
6535 1
< 0.1%
6488 1
< 0.1%
6466 1
< 0.1%

blueCSPerMin
Real number (ℝ)

Distinct148
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.669956
Minimum9
Maximum28.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:08.662680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile18
Q120.2
median21.8
Q323.2
95-th percentile25.1
Maximum28.3
Range19.3
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1858437
Coefficient of variation (CV)0.10086978
Kurtosis0.17262236
Mean21.669956
Median Absolute Deviation (MAD)1.5
Skewness-0.26777078
Sum214077.5
Variance4.7779128
MonotonicityNot monotonic
2023-05-10T16:21:08.789654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.8 193
 
2.0%
22 192
 
1.9%
22.2 190
 
1.9%
22.9 186
 
1.9%
22.1 185
 
1.9%
21.4 183
 
1.9%
22.6 183
 
1.9%
21.5 175
 
1.8%
22.5 175
 
1.8%
22.3 175
 
1.8%
Other values (138) 8042
81.4%
ValueCountFrequency (%)
9 1
 
< 0.1%
12 1
 
< 0.1%
12.3 1
 
< 0.1%
13 1
 
< 0.1%
13.1 1
 
< 0.1%
13.6 1
 
< 0.1%
13.7 1
 
< 0.1%
13.8 2
< 0.1%
14 1
 
< 0.1%
14.1 3
< 0.1%
ValueCountFrequency (%)
28.3 1
 
< 0.1%
28.1 1
 
< 0.1%
27.9 2
 
< 0.1%
27.6 6
0.1%
27.5 1
 
< 0.1%
27.4 1
 
< 0.1%
27.3 1
 
< 0.1%
27.2 6
0.1%
27.1 6
0.1%
27 9
0.1%

blueGoldPerMin
Real number (ℝ)

Distinct4739
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1650.3456
Minimum1073
Maximum2370.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:08.923728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1073
5-th percentile1419.4
Q11541.55
median1639.8
Q31745.9
95-th percentile1919.05
Maximum2370.1
Range1297.1
Interquartile range (IQR)204.35

Descriptive statistics

Standard deviation153.54466
Coefficient of variation (CV)0.093037887
Kurtosis0.47931151
Mean1650.3456
Median Absolute Deviation (MAD)101.6
Skewness0.46824752
Sum16303764
Variance23575.964
MonotonicityNot monotonic
2023-05-10T16:21:09.056788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1588.5 9
 
0.1%
1674.9 9
 
0.1%
1695.6 9
 
0.1%
1596.7 8
 
0.1%
1795.1 8
 
0.1%
1694 8
 
0.1%
1564.9 8
 
0.1%
1668.8 8
 
0.1%
1629.1 7
 
0.1%
1697.2 7
 
0.1%
Other values (4729) 9798
99.2%
ValueCountFrequency (%)
1073 1
< 0.1%
1200.2 1
< 0.1%
1217.8 1
< 0.1%
1229.2 1
< 0.1%
1230 1
< 0.1%
1240.3 1
< 0.1%
1251.9 1
< 0.1%
1259.8 1
< 0.1%
1262.2 1
< 0.1%
1268.2 1
< 0.1%
ValueCountFrequency (%)
2370.1 1
< 0.1%
2342.4 1
< 0.1%
2335.9 1
< 0.1%
2334.9 1
< 0.1%
2333.5 1
< 0.1%
2327.8 1
< 0.1%
2320.5 1
< 0.1%
2284.5 1
< 0.1%
2274.5 1
< 0.1%
2269.7 1
< 0.1%

redWardsPlaced
Real number (ℝ)

Distinct151
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.367952
Minimum6
Maximum276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:09.189527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile12
Q114
median16
Q320
95-th percentile53
Maximum276
Range270
Interquartile range (IQR)6

Descriptive statistics

Standard deviation18.457427
Coefficient of variation (CV)0.82517285
Kurtosis30.474008
Mean22.367952
Median Absolute Deviation (MAD)2
Skewness4.56066
Sum220973
Variance340.6766
MonotonicityNot monotonic
2023-05-10T16:21:09.308157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 1212
12.3%
16 1206
12.2%
17 1055
10.7%
14 1038
10.5%
18 733
 
7.4%
13 684
 
6.9%
19 521
 
5.3%
12 453
 
4.6%
20 302
 
3.1%
11 233
 
2.4%
Other values (141) 2442
24.7%
ValueCountFrequency (%)
6 2
 
< 0.1%
7 6
 
0.1%
8 8
 
0.1%
9 40
 
0.4%
10 94
 
1.0%
11 233
 
2.4%
12 453
 
4.6%
13 684
6.9%
14 1038
10.5%
15 1212
12.3%
ValueCountFrequency (%)
276 1
< 0.1%
268 1
< 0.1%
230 1
< 0.1%
216 1
< 0.1%
213 1
< 0.1%
207 1
< 0.1%
205 1
< 0.1%
203 1
< 0.1%
193 1
< 0.1%
191 1
< 0.1%

redWardsDestroyed
Real number (ℝ)

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7231501
Minimum0
Maximum24
Zeros785
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:09.419080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile6
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1383561
Coefficient of variation (CV)0.78525091
Kurtosis18.237031
Mean2.7231501
Median Absolute Deviation (MAD)1
Skewness2.9490995
Sum26902
Variance4.5725669
MonotonicityNot monotonic
2023-05-10T16:21:09.519916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2 2421
24.5%
3 2026
20.5%
1 1951
19.7%
4 1346
13.6%
0 785
 
7.9%
5 736
 
7.5%
6 316
 
3.2%
7 120
 
1.2%
8 42
 
0.4%
9 26
 
0.3%
Other values (15) 110
 
1.1%
ValueCountFrequency (%)
0 785
 
7.9%
1 1951
19.7%
2 2421
24.5%
3 2026
20.5%
4 1346
13.6%
5 736
 
7.5%
6 316
 
3.2%
7 120
 
1.2%
8 42
 
0.4%
9 26
 
0.3%
ValueCountFrequency (%)
24 3
 
< 0.1%
23 1
 
< 0.1%
22 3
 
< 0.1%
21 3
 
< 0.1%
20 6
0.1%
19 6
0.1%
18 8
0.1%
17 8
0.1%
16 5
0.1%
15 9
0.1%

redFirstBlood
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
0
4987 
1
4892 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 4987
50.5%
1 4892
49.5%

Length

2023-05-10T16:21:09.626792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T16:21:09.728534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4987
50.5%
1 4892
49.5%

Most occurring characters

ValueCountFrequency (%)
0 4987
50.5%
1 4892
49.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4987
50.5%
1 4892
49.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4987
50.5%
1 4892
49.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4987
50.5%
1 4892
49.5%

redKills
Real number (ℝ)

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1376658
Minimum0
Maximum22
Zeros72
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:09.809623image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median6
Q38
95-th percentile11
Maximum22
Range22
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9338177
Coefficient of variation (CV)0.4780022
Kurtosis0.21409761
Mean6.1376658
Median Absolute Deviation (MAD)2
Skewness0.50749282
Sum60634
Variance8.6072864
MonotonicityNot monotonic
2023-05-10T16:21:09.898668image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
5 1341
13.6%
6 1293
13.1%
4 1221
12.4%
7 1188
12.0%
8 942
9.5%
3 934
9.5%
9 734
7.4%
2 603
6.1%
10 494
 
5.0%
11 331
 
3.4%
Other values (11) 798
8.1%
ValueCountFrequency (%)
0 72
 
0.7%
1 270
 
2.7%
2 603
6.1%
3 934
9.5%
4 1221
12.4%
5 1341
13.6%
6 1293
13.1%
7 1188
12.0%
8 942
9.5%
9 734
7.4%
ValueCountFrequency (%)
22 1
 
< 0.1%
19 2
 
< 0.1%
18 2
 
< 0.1%
17 8
 
0.1%
16 20
 
0.2%
15 32
 
0.3%
14 66
 
0.7%
13 114
 
1.2%
12 211
2.1%
11 331
3.4%

redDeaths
Real number (ℝ)

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1839255
Minimum0
Maximum22
Zeros63
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:10.001847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median6
Q38
95-th percentile12
Maximum22
Range22
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.011028
Coefficient of variation (CV)0.48691207
Kurtosis0.2637882
Mean6.1839255
Median Absolute Deviation (MAD)2
Skewness0.53851754
Sum61091
Variance9.0662895
MonotonicityNot monotonic
2023-05-10T16:21:10.101207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
6 1322
13.4%
5 1302
13.2%
4 1186
12.0%
7 1138
11.5%
8 942
9.5%
3 917
9.3%
9 717
7.3%
2 609
6.2%
10 527
 
5.3%
11 340
 
3.4%
Other values (11) 879
8.9%
ValueCountFrequency (%)
0 63
 
0.6%
1 313
 
3.2%
2 609
6.2%
3 917
9.3%
4 1186
12.0%
5 1302
13.2%
6 1322
13.4%
7 1138
11.5%
8 942
9.5%
9 717
7.3%
ValueCountFrequency (%)
22 1
 
< 0.1%
19 2
 
< 0.1%
18 4
 
< 0.1%
17 13
 
0.1%
16 30
 
0.3%
15 38
 
0.4%
14 64
 
0.6%
13 147
1.5%
12 204
2.1%
11 340
3.4%

redAssists
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6621115
Minimum0
Maximum28
Zeros235
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:10.200574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median6
Q39
95-th percentile14
Maximum28
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.0606124
Coefficient of variation (CV)0.60950832
Kurtosis0.78540961
Mean6.6621115
Median Absolute Deviation (MAD)3
Skewness0.82337611
Sum65815
Variance16.488573
MonotonicityNot monotonic
2023-05-10T16:21:10.293017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
5 1043
10.6%
4 993
10.1%
6 981
9.9%
7 916
9.3%
3 911
9.2%
8 779
7.9%
2 707
 
7.2%
9 666
 
6.7%
10 563
 
5.7%
1 472
 
4.8%
Other values (18) 1848
18.7%
ValueCountFrequency (%)
0 235
 
2.4%
1 472
4.8%
2 707
7.2%
3 911
9.2%
4 993
10.1%
5 1043
10.6%
6 981
9.9%
7 916
9.3%
8 779
7.9%
9 666
6.7%
ValueCountFrequency (%)
28 1
 
< 0.1%
26 1
 
< 0.1%
25 2
 
< 0.1%
24 5
 
0.1%
23 9
 
0.1%
22 9
 
0.1%
21 13
 
0.1%
20 25
0.3%
19 33
0.3%
18 46
0.5%

redEliteMonsters
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
0
4947 
1
4202 
2
730 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 4947
50.1%
1 4202
42.5%
2 730
 
7.4%

Length

2023-05-10T16:21:10.390604image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T16:21:10.478511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 4947
50.1%
1 4202
42.5%
2 730
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 4947
50.1%
1 4202
42.5%
2 730
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4947
50.1%
1 4202
42.5%
2 730
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4947
50.1%
1 4202
42.5%
2 730
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4947
50.1%
1 4202
42.5%
2 730
 
7.4%

redDragons
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
0
5798 
1
4081 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 5798
58.7%
1 4081
41.3%

Length

2023-05-10T16:21:10.563128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T16:21:10.658825image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5798
58.7%
1 4081
41.3%

Most occurring characters

ValueCountFrequency (%)
0 5798
58.7%
1 4081
41.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5798
58.7%
1 4081
41.3%

Most occurring scripts

ValueCountFrequency (%)
Common 9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5798
58.7%
1 4081
41.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5798
58.7%
1 4081
41.3%

redHeralds
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
0
8298 
1
1581 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8298
84.0%
1 1581
 
16.0%

Length

2023-05-10T16:21:10.748969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T16:21:10.853111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 8298
84.0%
1 1581
 
16.0%

Most occurring characters

ValueCountFrequency (%)
0 8298
84.0%
1 1581
 
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8298
84.0%
1 1581
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8298
84.0%
1 1581
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8298
84.0%
1 1581
 
16.0%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
0
9483 
1
 
367
2
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9879
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9483
96.0%
1 367
 
3.7%
2 29
 
0.3%

Length

2023-05-10T16:21:10.931857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T16:21:11.027321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 9483
96.0%
1 367
 
3.7%
2 29
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 9483
96.0%
1 367
 
3.7%
2 29
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9879
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9483
96.0%
1 367
 
3.7%
2 29
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 9879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9483
96.0%
1 367
 
3.7%
2 29
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9483
96.0%
1 367
 
3.7%
2 29
 
0.3%

redTotalGold
Real number (ℝ)

Distinct4732
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16489.041
Minimum11212
Maximum22732
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:11.119745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum11212
5-th percentile14238.8
Q115427.5
median16378
Q317418.5
95-th percentile19137
Maximum22732
Range11520
Interquartile range (IQR)1991

Descriptive statistics

Standard deviation1490.8884
Coefficient of variation (CV)0.090416924
Kurtosis0.21900015
Mean16489.041
Median Absolute Deviation (MAD)989
Skewness0.41074316
Sum1.6289524 × 108
Variance2222748.2
MonotonicityNot monotonic
2023-05-10T16:21:11.237320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16074 9
 
0.1%
16561 8
 
0.1%
16379 8
 
0.1%
17404 8
 
0.1%
16154 8
 
0.1%
16038 8
 
0.1%
16553 8
 
0.1%
15881 8
 
0.1%
17271 7
 
0.1%
16873 7
 
0.1%
Other values (4722) 9800
99.2%
ValueCountFrequency (%)
11212 1
< 0.1%
11357 1
< 0.1%
11502 1
< 0.1%
11957 1
< 0.1%
12275 1
< 0.1%
12338 1
< 0.1%
12626 1
< 0.1%
12651 1
< 0.1%
12724 1
< 0.1%
12725 1
< 0.1%
ValueCountFrequency (%)
22732 1
< 0.1%
22681 1
< 0.1%
22614 1
< 0.1%
22402 1
< 0.1%
22355 1
< 0.1%
22283 1
< 0.1%
22250 1
< 0.1%
22110 1
< 0.1%
22088 1
< 0.1%
22073 1
< 0.1%

redAvgLevel
Real number (ℝ)

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9253163
Minimum4.8
Maximum8.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:11.344607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum4.8
5-th percentile6.4
Q16.8
median7
Q37.2
95-th percentile7.4
Maximum8.2
Range3.4
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.30531142
Coefficient of variation (CV)0.044086278
Kurtosis1.2369499
Mean6.9253163
Median Absolute Deviation (MAD)0.2
Skewness-0.39810927
Sum68415.2
Variance0.093215062
MonotonicityNot monotonic
2023-05-10T16:21:11.444511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
7 2672
27.0%
6.8 2392
24.2%
7.2 1838
18.6%
6.6 1275
12.9%
7.4 713
 
7.2%
6.4 540
 
5.5%
7.6 196
 
2.0%
6.2 150
 
1.5%
6 52
 
0.5%
5.8 18
 
0.2%
Other values (8) 33
 
0.3%
ValueCountFrequency (%)
4.8 2
 
< 0.1%
5 1
 
< 0.1%
5.2 1
 
< 0.1%
5.4 3
 
< 0.1%
5.6 5
 
0.1%
5.8 18
 
0.2%
6 52
 
0.5%
6.2 150
 
1.5%
6.4 540
5.5%
6.6 1275
12.9%
ValueCountFrequency (%)
8.2 1
 
< 0.1%
8 3
 
< 0.1%
7.8 17
 
0.2%
7.6 196
 
2.0%
7.4 713
 
7.2%
7.2 1838
18.6%
7 2672
27.0%
6.8 2392
24.2%
6.6 1275
12.9%
6.4 540
 
5.5%

redTotalExperience
Real number (ℝ)

Distinct4113
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17961.73
Minimum10465
Maximum22269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:11.569265image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10465
5-th percentile15962
Q117209.5
median17974
Q318764.5
95-th percentile19879
Maximum22269
Range11804
Interquartile range (IQR)1555

Descriptive statistics

Standard deviation1198.5839
Coefficient of variation (CV)0.066729869
Kurtosis0.8169216
Mean17961.73
Median Absolute Deviation (MAD)776
Skewness-0.28153105
Sum1.7744394 × 108
Variance1436603.4
MonotonicityNot monotonic
2023-05-10T16:21:11.709303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17842 10
 
0.1%
17501 9
 
0.1%
17608 9
 
0.1%
17680 9
 
0.1%
17212 9
 
0.1%
18534 9
 
0.1%
18308 9
 
0.1%
17505 9
 
0.1%
17696 9
 
0.1%
18037 9
 
0.1%
Other values (4103) 9788
99.1%
ValueCountFrequency (%)
10465 1
< 0.1%
10610 1
< 0.1%
11351 1
< 0.1%
11443 1
< 0.1%
11999 1
< 0.1%
12397 1
< 0.1%
12846 1
< 0.1%
12983 1
< 0.1%
13105 1
< 0.1%
13264 1
< 0.1%
ValueCountFrequency (%)
22269 1
< 0.1%
22258 1
< 0.1%
21967 1
< 0.1%
21818 1
< 0.1%
21774 1
< 0.1%
21728 1
< 0.1%
21722 1
< 0.1%
21644 1
< 0.1%
21641 1
< 0.1%
21639 1
< 0.1%

redTotalMinionsKilled
Real number (ℝ)

Distinct153
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217.34923
Minimum107
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:11.846066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum107
5-th percentile180
Q1203
median218
Q3233
95-th percentile252
Maximum289
Range182
Interquartile range (IQR)30

Descriptive statistics

Standard deviation21.911668
Coefficient of variation (CV)0.10081319
Kurtosis0.22670485
Mean217.34923
Median Absolute Deviation (MAD)15
Skewness-0.28931077
Sum2147193
Variance480.1212
MonotonicityNot monotonic
2023-05-10T16:21:11.983375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
215 198
 
2.0%
218 192
 
1.9%
220 191
 
1.9%
225 188
 
1.9%
221 184
 
1.9%
214 179
 
1.8%
226 179
 
1.8%
211 174
 
1.8%
216 174
 
1.8%
210 174
 
1.8%
Other values (143) 8046
81.4%
ValueCountFrequency (%)
107 1
< 0.1%
117 1
< 0.1%
123 1
< 0.1%
129 2
< 0.1%
132 2
< 0.1%
133 1
< 0.1%
134 1
< 0.1%
135 1
< 0.1%
136 1
< 0.1%
137 1
< 0.1%
ValueCountFrequency (%)
289 2
 
< 0.1%
282 1
 
< 0.1%
280 1
 
< 0.1%
279 1
 
< 0.1%
278 1
 
< 0.1%
277 1
 
< 0.1%
276 4
< 0.1%
275 2
 
< 0.1%
274 2
 
< 0.1%
273 6
0.1%
Distinct75
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.313088
Minimum4
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:12.120897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile36
Q144
median51
Q357
95-th percentile68
Maximum92
Range88
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.027885
Coefficient of variation (CV)0.19542548
Kurtosis0.41562501
Mean51.313088
Median Absolute Deviation (MAD)7
Skewness0.23122917
Sum506922
Variance100.55848
MonotonicityNot monotonic
2023-05-10T16:21:12.241570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 894
 
9.0%
48 850
 
8.6%
56 719
 
7.3%
44 686
 
6.9%
60 549
 
5.6%
40 475
 
4.8%
64 317
 
3.2%
51 310
 
3.1%
47 296
 
3.0%
55 265
 
2.7%
Other values (65) 4518
45.7%
ValueCountFrequency (%)
4 1
 
< 0.1%
8 3
 
< 0.1%
11 2
 
< 0.1%
12 1
 
< 0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
20 3
 
< 0.1%
22 3
 
< 0.1%
23 2
 
< 0.1%
24 15
0.2%
ValueCountFrequency (%)
92 3
 
< 0.1%
91 1
 
< 0.1%
89 1
 
< 0.1%
88 4
 
< 0.1%
85 1
 
< 0.1%
84 10
 
0.1%
83 3
 
< 0.1%
82 3
 
< 0.1%
81 5
 
0.1%
80 37
0.4%

redGoldDiff
Real number (ℝ)

Distinct6047
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-14.414111
Minimum-11467
Maximum10830
Zeros2
Zeros (%)< 0.1%
Negative4960
Negative (%)50.2%
Memory size77.3 KiB
2023-05-10T16:21:12.370591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-11467
5-th percentile-4074
Q1-1596
median-14
Q31585.5
95-th percentile4033.2
Maximum10830
Range22297
Interquartile range (IQR)3181.5

Descriptive statistics

Standard deviation2453.3492
Coefficient of variation (CV)-170.20469
Kurtosis0.2994089
Mean-14.414111
Median Absolute Deviation (MAD)1592
Skewness-0.030037509
Sum-142397
Variance6018922.2
MonotonicityNot monotonic
2023-05-10T16:21:12.491468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-428 8
 
0.1%
-1167 7
 
0.1%
1806 7
 
0.1%
839 6
 
0.1%
27 6
 
0.1%
-1060 6
 
0.1%
635 6
 
0.1%
-611 6
 
0.1%
152 6
 
0.1%
1208 6
 
0.1%
Other values (6037) 9815
99.4%
ValueCountFrequency (%)
-11467 1
< 0.1%
-8977 1
< 0.1%
-8863 1
< 0.1%
-8776 1
< 0.1%
-8667 1
< 0.1%
-8657 1
< 0.1%
-8553 1
< 0.1%
-8532 1
< 0.1%
-8450 1
< 0.1%
-8347 1
< 0.1%
ValueCountFrequency (%)
10830 1
< 0.1%
10329 1
< 0.1%
9341 1
< 0.1%
9152 1
< 0.1%
8472 1
< 0.1%
8461 1
< 0.1%
7952 1
< 0.1%
7911 1
< 0.1%
7868 1
< 0.1%
7866 1
< 0.1%

redExperienceDiff
Real number (ℝ)

Distinct5356
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.620306
Minimum-8348
Maximum9333
Zeros1
Zeros (%)< 0.1%
Negative4864
Negative (%)49.2%
Memory size77.3 KiB
2023-05-10T16:21:12.610279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-8348
5-th percentile-3109.3
Q1-1212
median28
Q31290.5
95-th percentile3206.1
Maximum9333
Range17681
Interquartile range (IQR)2502.5

Descriptive statistics

Standard deviation1920.3704
Coefficient of variation (CV)57.119363
Kurtosis0.36484788
Mean33.620306
Median Absolute Deviation (MAD)1252
Skewness-0.022876036
Sum332135
Variance3687822.6
MonotonicityNot monotonic
2023-05-10T16:21:12.726579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-63 8
 
0.1%
1025 7
 
0.1%
-411 7
 
0.1%
298 7
 
0.1%
226 7
 
0.1%
29 7
 
0.1%
1476 7
 
0.1%
953 6
 
0.1%
-1187 6
 
0.1%
213 6
 
0.1%
Other values (5346) 9811
99.3%
ValueCountFrequency (%)
-8348 1
< 0.1%
-8265 1
< 0.1%
-7645 1
< 0.1%
-7621 1
< 0.1%
-7609 1
< 0.1%
-6703 1
< 0.1%
-6558 1
< 0.1%
-6535 1
< 0.1%
-6488 1
< 0.1%
-6466 1
< 0.1%
ValueCountFrequency (%)
9333 1
< 0.1%
8531 1
< 0.1%
8290 1
< 0.1%
8242 1
< 0.1%
7340 1
< 0.1%
6488 1
< 0.1%
6414 1
< 0.1%
6365 1
< 0.1%
6317 1
< 0.1%
6210 1
< 0.1%

redCSPerMin
Real number (ℝ)

Distinct153
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.734923
Minimum10.7
Maximum28.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:12.867132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10.7
5-th percentile18
Q120.3
median21.8
Q323.3
95-th percentile25.2
Maximum28.9
Range18.2
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1911668
Coefficient of variation (CV)0.10081319
Kurtosis0.22670485
Mean21.734923
Median Absolute Deviation (MAD)1.5
Skewness-0.28931077
Sum214719.3
Variance4.801212
MonotonicityNot monotonic
2023-05-10T16:21:12.999513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.5 198
 
2.0%
21.8 192
 
1.9%
22 191
 
1.9%
22.5 188
 
1.9%
22.1 184
 
1.9%
21.4 179
 
1.8%
22.6 179
 
1.8%
21.1 174
 
1.8%
21.6 174
 
1.8%
21 174
 
1.8%
Other values (143) 8046
81.4%
ValueCountFrequency (%)
10.7 1
< 0.1%
11.7 1
< 0.1%
12.3 1
< 0.1%
12.9 2
< 0.1%
13.2 2
< 0.1%
13.3 1
< 0.1%
13.4 1
< 0.1%
13.5 1
< 0.1%
13.6 1
< 0.1%
13.7 1
< 0.1%
ValueCountFrequency (%)
28.9 2
 
< 0.1%
28.2 1
 
< 0.1%
28 1
 
< 0.1%
27.9 1
 
< 0.1%
27.8 1
 
< 0.1%
27.7 1
 
< 0.1%
27.6 4
< 0.1%
27.5 2
 
< 0.1%
27.4 2
 
< 0.1%
27.3 6
0.1%

redGoldPerMin
Real number (ℝ)

Distinct4732
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1648.9041
Minimum1121.2
Maximum2273.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.3 KiB
2023-05-10T16:21:13.134303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1121.2
5-th percentile1423.88
Q11542.75
median1637.8
Q31741.85
95-th percentile1913.7
Maximum2273.2
Range1152
Interquartile range (IQR)199.1

Descriptive statistics

Standard deviation149.08884
Coefficient of variation (CV)0.090416924
Kurtosis0.21900015
Mean1648.9041
Median Absolute Deviation (MAD)98.9
Skewness0.41074316
Sum16289524
Variance22227.482
MonotonicityNot monotonic
2023-05-10T16:21:13.261885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1607.4 9
 
0.1%
1656.1 8
 
0.1%
1637.9 8
 
0.1%
1740.4 8
 
0.1%
1615.4 8
 
0.1%
1603.8 8
 
0.1%
1655.3 8
 
0.1%
1588.1 8
 
0.1%
1727.1 7
 
0.1%
1687.3 7
 
0.1%
Other values (4722) 9800
99.2%
ValueCountFrequency (%)
1121.2 1
< 0.1%
1135.7 1
< 0.1%
1150.2 1
< 0.1%
1195.7 1
< 0.1%
1227.5 1
< 0.1%
1233.8 1
< 0.1%
1262.6 1
< 0.1%
1265.1 1
< 0.1%
1272.4 1
< 0.1%
1272.5 1
< 0.1%
ValueCountFrequency (%)
2273.2 1
< 0.1%
2268.1 1
< 0.1%
2261.4 1
< 0.1%
2240.2 1
< 0.1%
2235.5 1
< 0.1%
2228.3 1
< 0.1%
2225 1
< 0.1%
2211 1
< 0.1%
2208.8 1
< 0.1%
2207.3 1
< 0.1%

Interactions

2023-05-10T16:20:57.568123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:17.150920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:20.758454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:23.894645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:27.162296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:30.669405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:34.317975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:38.178800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:43.284045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:46.717180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:50.148454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:53.730745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:56.711822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:59.995952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:04.224945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:07.699853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:10.821500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:13.940876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:18.261594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:21.252672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:24.382331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:28.100915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:31.782876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:38.039911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:41.697170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:45.379633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:50.084708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:54.189277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:57.703560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:17.268984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:20.897279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:24.005545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:27.263025image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:30.796384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:34.438229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:38.319838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:43.393475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:46.831353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:50.244637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:53.842186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:56.820993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:00.110892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:04.335927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:07.803706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:10.939169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:14.043936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:18.368934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:21.370001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:24.490433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:28.291564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:31.910066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:38.174348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:41.813011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:45.515859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:50.224729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:54.321721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:57.814759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:17.375723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:20.999864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:24.130790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:27.355336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:30.902036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:34.554621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:38.554404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:43.553079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:46.940261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:50.341273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:53.963557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:56.935728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:00.220108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:04.446990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:07.908239image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:11.050171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:14.266228image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:18.469335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:21.472931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:24.624100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:28.428132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:32.039349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:38.293556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:41.933800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:45.689797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:50.379388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:54.451456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:57.925723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:17.470868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:21.105082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:24.235164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:27.467796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:31.005572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:34.664396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:38.719375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:43.696043image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:47.048959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:50.436423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:54.059658image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:57.041325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:00.320463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:04.581517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:08.013025image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-05-10T16:20:57.087387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:21:00.382323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:20.399328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:23.563004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:26.845941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:30.346088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:33.950297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:37.673079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:42.939369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:46.378606image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:49.806142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:53.409464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:56.384773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:59.627583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:03.842275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:07.363566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:10.481076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:13.600055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:17.908246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:20.921243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:24.044293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:27.429172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:31.429126image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:35.327721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:41.351958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:44.672974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:49.606924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:53.773423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:57.202502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:21:00.497829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:20.505012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:23.665873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:26.952369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:30.455101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:34.089847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:37.846756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:43.061605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:46.491994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:49.926998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:53.516993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:56.496536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:59.746390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:03.973617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:07.477397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:10.592001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:13.721711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:18.044149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:21.037000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:24.159879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:27.644155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:31.543439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:35.469022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:41.473759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:44.917404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:49.736969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:53.924855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:57.323594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:21:00.612625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:20.636497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:23.783595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:27.060334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:30.567841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:34.208179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:38.011214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:43.174316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:46.605899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:50.041055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:53.623371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:56.606386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:19:59.877378image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:04.103629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:07.586725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:10.703706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:13.831655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:18.159295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:21.151693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:24.275092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:27.864293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:31.669344image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:37.857544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:41.585624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:45.216501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:49.921854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:54.074064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-10T16:20:57.441078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-05-10T16:21:13.462733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
blueWardsPlacedblueWardsDestroyedblueKillsblueDeathsblueAssistsblueTotalGoldblueAvgLevelblueTotalExperienceblueTotalMinionsKilledblueTotalJungleMinionsKilledblueGoldDiffblueExperienceDiffblueCSPerMinblueGoldPerMinredWardsPlacedredWardsDestroyedredKillsredDeathsredAssistsredTotalGoldredAvgLevelredTotalExperienceredTotalMinionsKilledredTotalJungleMinionsKilledredGoldDiffredExperienceDiffredCSPerMinredGoldPerMinblueWinsblueFirstBloodblueEliteMonstersblueDragonsblueHeraldsblueTowersDestroyedredFirstBloodredEliteMonstersredDragonsredHeraldsredTowersDestroyed
blueWardsPlaced1.0000.1290.052-0.0600.0850.0600.0490.0430.0090.0040.0760.0820.0090.0600.0290.270-0.0600.052-0.035-0.060-0.079-0.088-0.007-0.029-0.076-0.082-0.007-0.0600.0000.0000.0110.0140.0110.0230.0000.0180.0300.0200.000
blueWardsDestroyed0.1291.0000.042-0.0780.0810.0700.0640.0700.127-0.0210.0960.1010.1270.0700.2900.193-0.0780.042-0.047-0.081-0.088-0.0890.019-0.042-0.096-0.1010.019-0.0810.0500.0000.0300.0440.0390.0000.0000.0210.0320.0220.000
blueKills0.0520.0421.0000.0060.8190.8850.4310.465-0.034-0.1160.6400.567-0.0340.885-0.094-0.0910.0061.000-0.018-0.154-0.391-0.442-0.452-0.208-0.640-0.567-0.452-0.1540.3300.2730.1240.1660.0710.1100.2730.1550.2010.0990.065
blueDeaths-0.060-0.0780.0061.000-0.027-0.156-0.395-0.442-0.454-0.216-0.628-0.563-0.454-0.1560.0320.0601.0000.0060.8080.8850.4260.454-0.043-0.1050.6280.563-0.0430.8850.3370.2490.1410.1820.0900.0240.2490.1150.1480.0750.121
blueAssists0.0850.0810.819-0.0271.0000.7540.3040.313-0.059-0.1380.5480.435-0.0590.754-0.063-0.059-0.0270.819-0.019-0.133-0.345-0.382-0.326-0.157-0.548-0.435-0.326-0.1330.2740.2310.1050.1700.0150.0800.2310.1280.1870.0530.045
blueTotalGold0.0600.0700.885-0.1560.7541.0000.6090.6700.2690.0790.8010.7110.2691.000-0.091-0.099-0.1560.885-0.128-0.299-0.418-0.462-0.425-0.164-0.801-0.711-0.425-0.2990.4130.3140.1670.1820.1490.2850.3140.1630.2010.1180.141
blueAvgLevel0.0490.0640.431-0.3950.3040.6091.0000.8870.4770.3480.6370.7030.4770.609-0.080-0.091-0.3950.431-0.349-0.419-0.224-0.245-0.121-0.006-0.637-0.703-0.121-0.4190.3500.1750.1360.1540.1170.0840.1750.1150.1310.0990.198
blueTotalExperience0.0430.0700.465-0.4420.3130.6700.8871.0000.5440.3880.7040.7910.5440.670-0.089-0.102-0.4420.465-0.381-0.465-0.250-0.276-0.136-0.001-0.704-0.791-0.136-0.4650.3810.1810.1600.1700.1420.1010.1810.1340.1400.1210.204
blueTotalMinionsKilled0.0090.127-0.034-0.454-0.0590.2690.4770.5441.0000.1630.4280.4231.0000.2690.0040.001-0.454-0.034-0.332-0.428-0.132-0.1310.0080.101-0.428-0.4230.008-0.4280.2190.1200.0820.0790.0830.0530.1200.0520.0550.0480.124
blueTotalJungleMinionsKilled0.004-0.021-0.116-0.216-0.1380.0790.3480.3880.1631.0000.1550.2490.1630.079-0.015-0.038-0.216-0.116-0.160-0.173-0.010-0.0100.108-0.013-0.155-0.2490.108-0.1730.1200.0250.1400.1530.1170.0000.0250.0470.0430.0520.071
blueGoldDiff0.0760.0960.640-0.6280.5480.8010.6370.7040.4280.1551.0000.8840.4280.801-0.082-0.119-0.6280.640-0.528-0.788-0.627-0.692-0.426-0.154-1.000-0.884-0.426-0.7880.5000.3670.1930.2220.1580.5560.3670.1970.2280.1650.286
blueExperienceDiff0.0820.1010.567-0.5630.4350.7110.7030.7910.4230.2490.8841.0000.4230.711-0.083-0.113-0.5630.567-0.420-0.703-0.696-0.783-0.409-0.249-0.884-1.000-0.409-0.7030.4780.2300.1850.2060.1550.2970.2300.1870.2090.1650.183
blueCSPerMin0.0090.127-0.034-0.454-0.0590.2690.4770.5441.0000.1630.4280.4231.0000.2690.0040.001-0.454-0.034-0.332-0.428-0.132-0.1310.0080.101-0.428-0.4230.008-0.4280.2190.1200.0820.0790.0830.0530.1200.0520.0550.0480.124
blueGoldPerMin0.0600.0700.885-0.1560.7541.0000.6090.6700.2690.0790.8010.7110.2691.000-0.091-0.099-0.1560.885-0.128-0.299-0.418-0.462-0.425-0.164-0.801-0.711-0.425-0.2990.4130.3140.1670.1820.1490.2850.3140.1630.2010.1180.141
redWardsPlaced0.0290.290-0.0940.032-0.063-0.091-0.080-0.0890.004-0.015-0.082-0.0830.004-0.0911.0000.1320.032-0.0940.0700.0450.0480.0420.033-0.0070.0820.0830.0330.0450.0140.0000.0000.0000.0200.0000.0000.0190.0000.0350.012
redWardsDestroyed0.2700.193-0.0910.060-0.059-0.099-0.091-0.1020.001-0.038-0.119-0.1130.001-0.0990.1321.0000.060-0.0910.0770.0940.0740.0770.141-0.0220.1190.1130.1410.0940.0680.0450.0110.0500.0000.0210.0450.0390.0650.0000.000
redKills-0.060-0.0780.0061.000-0.027-0.156-0.395-0.442-0.454-0.216-0.628-0.563-0.454-0.1560.0320.0601.0000.0060.8080.8850.4260.454-0.043-0.1050.6280.563-0.0430.8850.3370.2490.1410.1820.0900.0240.2490.1150.1480.0750.121
redDeaths0.0520.0421.0000.0060.8190.8850.4310.465-0.034-0.1160.6400.567-0.0340.885-0.094-0.0910.0061.000-0.018-0.154-0.391-0.442-0.452-0.208-0.640-0.567-0.452-0.1540.3300.2730.1240.1660.0710.1100.2730.1550.2010.0990.065
redAssists-0.035-0.047-0.0180.808-0.019-0.128-0.349-0.381-0.332-0.160-0.528-0.420-0.332-0.1280.0700.0770.808-0.0181.0000.7460.2840.286-0.075-0.1360.5280.420-0.0750.7460.2700.2040.1090.1610.0450.0040.2040.0940.1380.0290.077
redTotalGold-0.060-0.081-0.1540.885-0.133-0.299-0.419-0.465-0.428-0.173-0.788-0.703-0.428-0.2990.0450.0940.885-0.1540.7461.0000.5980.6540.2610.0870.7880.7030.2611.0000.4080.2990.1500.1860.1070.2280.2990.1630.1770.1500.313
redAvgLevel-0.079-0.088-0.3910.426-0.345-0.418-0.224-0.250-0.132-0.010-0.627-0.696-0.132-0.4180.0480.0740.426-0.3910.2840.5981.0000.8830.4560.3570.6270.6960.4560.5980.3460.1770.1190.1450.0840.3470.1770.1540.1850.1190.086
redTotalExperience-0.088-0.089-0.4420.454-0.382-0.462-0.245-0.276-0.131-0.010-0.692-0.783-0.131-0.4620.0420.0770.454-0.4420.2860.6540.8831.0000.5290.4040.6920.7830.5290.6540.3740.1860.1320.1510.1040.3270.1860.1680.1940.1350.096
redTotalMinionsKilled-0.0070.019-0.452-0.043-0.326-0.425-0.121-0.1360.0080.108-0.426-0.4090.008-0.4250.0330.141-0.043-0.452-0.0750.2610.4560.5291.0000.1560.4260.4091.0000.2610.2060.1530.0540.0640.0420.3310.1530.0900.0990.0800.066
redTotalJungleMinionsKilled-0.029-0.042-0.208-0.105-0.157-0.164-0.006-0.0010.101-0.013-0.154-0.2490.101-0.164-0.007-0.022-0.105-0.208-0.1360.0870.3570.4040.1561.0000.1540.2490.1560.0870.1040.0270.0650.0950.0000.0840.0270.1490.2070.0800.020
redGoldDiff-0.076-0.096-0.6400.628-0.548-0.801-0.637-0.704-0.428-0.155-1.000-0.884-0.428-0.8010.0820.1190.628-0.6400.5280.7880.6270.6920.4260.1541.0000.8840.4260.7880.5000.3670.1930.2220.1580.5560.3670.1970.2280.1650.286
redExperienceDiff-0.082-0.101-0.5670.563-0.435-0.711-0.703-0.791-0.423-0.249-0.884-1.000-0.423-0.7110.0830.1130.563-0.5670.4200.7030.6960.7830.4090.2490.8841.0000.4090.7030.4780.2300.1850.2060.1550.2970.2300.1870.2090.1650.183
redCSPerMin-0.0070.019-0.452-0.043-0.326-0.425-0.121-0.1360.0080.108-0.426-0.4090.008-0.4250.0330.141-0.043-0.452-0.0750.2610.4560.5291.0000.1560.4260.4091.0000.2610.2050.1540.0540.0640.0420.3320.1540.0900.0990.0800.066
redGoldPerMin-0.060-0.081-0.1540.885-0.133-0.299-0.419-0.465-0.428-0.173-0.788-0.703-0.428-0.2990.0450.0940.885-0.1540.7461.0000.5980.6540.2610.0870.7880.7030.2611.0000.4090.2990.1510.1870.1070.2280.2990.1630.1770.1500.313
blueWins0.0000.0500.3300.3370.2740.4130.3500.3810.2190.1200.5000.4780.2190.4130.0140.0680.3370.3300.2700.4080.3460.3740.2060.1040.5000.4780.2050.4091.0000.2010.2220.2130.0920.1150.2010.2230.2090.0960.106
blueFirstBlood0.0000.0000.2730.2490.2310.3140.1750.1810.1200.0250.3670.2300.1200.3140.0000.0450.2490.2730.2040.2990.1770.1860.1530.0270.3670.2300.1540.2990.2011.0000.1510.1340.0770.0841.0000.1410.1350.0590.068
blueEliteMonsters0.0110.0300.1240.1410.1050.1670.1360.1600.0820.1400.1930.1850.0820.1670.0000.0110.1410.1240.1090.1500.1190.1320.0540.0650.1930.1850.0540.1510.2220.1511.0000.8010.6760.1260.1510.3330.4810.1480.034
blueDragons0.0140.0440.1660.1820.1700.1820.1540.1700.0790.1530.2220.2060.0790.1820.0000.0500.1820.1660.1610.1860.1450.1510.0640.0950.2220.2060.0640.1870.2130.1340.8011.0000.0170.0410.1340.5280.6320.0130.030
blueHeralds0.0110.0390.0710.0900.0150.1490.1170.1420.0830.1170.1580.1550.0830.1490.0200.0000.0900.0710.0450.1070.0840.1040.0420.0000.1580.1550.0420.1070.0920.0770.6760.0171.0000.2250.0770.1370.0190.2090.040
blueTowersDestroyed0.0230.0000.1100.0240.0800.2850.0840.1010.0530.0000.5560.2970.0530.2850.0000.0210.0240.1100.0040.2280.3470.3270.3310.0840.5560.2970.3320.2280.1150.0840.1260.0410.2251.0000.0840.0350.0250.0250.000
redFirstBlood0.0000.0000.2730.2490.2310.3140.1750.1810.1200.0250.3670.2300.1200.3140.0000.0450.2490.2730.2040.2990.1770.1860.1530.0270.3670.2300.1540.2990.2011.0000.1510.1340.0770.0841.0000.1410.1350.0590.068
redEliteMonsters0.0180.0210.1550.1150.1280.1630.1150.1340.0520.0470.1970.1870.0520.1630.0190.0390.1150.1550.0940.1630.1540.1680.0900.1490.1970.1870.0900.1630.2230.1410.3330.5280.1370.0350.1411.0000.8460.6990.125
redDragons0.0300.0320.2010.1480.1870.2010.1310.1400.0550.0430.2280.2090.0550.2010.0000.0650.1480.2010.1380.1770.1850.1940.0990.2070.2280.2090.0990.1770.2090.1350.4810.6320.0190.0250.1350.8461.0000.0420.027
redHeralds0.0200.0220.0990.0750.0530.1180.0990.1210.0480.0520.1650.1650.0480.1180.0350.0000.0750.0990.0290.1500.1190.1350.0800.0800.1650.1650.0800.1500.0960.0590.1480.0130.2090.0250.0590.6990.0421.0000.236
redTowersDestroyed0.0000.0000.0650.1210.0450.1410.1980.2040.1240.0710.2860.1830.1240.1410.0120.0000.1210.0650.0770.3130.0860.0960.0660.0200.2860.1830.0660.3130.1060.0680.0340.0300.0400.0000.0680.1250.0270.2361.000

Missing values

2023-05-10T16:21:00.813763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-10T16:21:01.555775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

blueWinsblueWardsPlacedblueWardsDestroyedblueFirstBloodblueKillsblueDeathsblueAssistsblueEliteMonstersblueDragonsblueHeraldsblueTowersDestroyedblueTotalGoldblueAvgLevelblueTotalExperienceblueTotalMinionsKilledblueTotalJungleMinionsKilledblueGoldDiffblueExperienceDiffblueCSPerMinblueGoldPerMinredWardsPlacedredWardsDestroyedredFirstBloodredKillsredDeathsredAssistsredEliteMonstersredDragonsredHeraldsredTowersDestroyedredTotalGoldredAvgLevelredTotalExperienceredTotalMinionsKilledredTotalJungleMinionsKilledredGoldDiffredExperienceDiffredCSPerMinredGoldPerMin
00282196110000172106.61703919536643-819.51721.015606980000165676.81704719755-643819.71656.7
1012105550000147126.61626517443-2908-117317.41471.212115522111176206.817438240522908117324.01762.0
20150071141100161136.41622118646-1172-103318.61611.31531117140000172856.817254203281172103320.31728.5
3043104551010151577.01795420155-1321-720.11515.7152154100000164787.017961235471321723.51647.8
4075406660000164007.01854321057-100423021.01640.017216671100174047.018313225671004-23022.51740.4
5118005361100158997.0181612254269810122.51589.936513520000152017.01806022159-698-10122.11520.1
6118317671100168746.816967225532411156322.51687.457106790000144636.41540416435-2411-156316.41446.3
70162051330000153056.41613820948-2615-80020.91530.51501135111100179206.61693815754261580015.71792.0
8016307780000164017.21852718961-1979-77118.91640.115217752110183807.21929824053197977124.01838.0
9113114551100150576.81680522039-1548-157422.01505.716205440000166056.818379247431548157424.71660.5
blueWinsblueWardsPlacedblueWardsDestroyedblueFirstBloodblueKillsblueDeathsblueAssistsblueEliteMonstersblueDragonsblueHeraldsblueTowersDestroyedblueTotalGoldblueAvgLevelblueTotalExperienceblueTotalMinionsKilledblueTotalJungleMinionsKilledblueGoldDiffblueExperienceDiffblueCSPerMinblueGoldPerMinredWardsPlacedredWardsDestroyedredFirstBloodredKillsredDeathsredAssistsredEliteMonstersredDragonsredHeraldsredTowersDestroyedredTotalGoldredAvgLevelredTotalExperienceredTotalMinionsKilledredTotalJungleMinionsKilledredGoldDiffredExperienceDiffredCSPerMinredGoldPerMin
986901210912121100161987.01824916533-2121-103816.51619.8133112971010183197.419287187682121103818.71831.9
9870146215320000169237.219758222721974171222.21692.311003551100149496.81804620264-1974-171220.21494.9
9871012204552110151316.81821621461-72734321.41513.117415420000158586.81787324848727-34324.81585.8
9872112017790000171557.01800223136756123.11715.560307781100163997.01800121658-756-121.61639.9
987311821126130000185737.219391207462639236420.71857.3166061260000159346.61702719738-2639-236419.71593.4
9874117217451100177657.218967211692519246921.11776.546304770000152466.81649822934-2519-246922.91524.6
9875154006481100162387.2192552334878288823.31623.8122114630000154567.01836720656-782-88820.61545.6
9876023106750000159037.01803221045-2416-187721.01590.3140176111100183197.419909261602416187726.11831.9
9877014412331100144596.61722922448-839-108522.41445.966403210000152987.21831424740839108524.71529.8
9878118016650000162667.01732120744927-5820.71626.69206641100153396.81737920146-9275820.11533.9